Tuberculosis treatment monitoring tests during routine practice: study design guidance

The current tools for tuberculosis (TB) treatment monitoring, smear microscopy and culture, cannot accurately predict poor treatment outcomes. Research into new TB treatment monitoring tools (TMTs) is growing, but data are unreliable. In this article, we aim to provide guidance for studies investigating and evaluating TB TMT for use during routine clinical care. Here, a TB TMT would guide treatment during the course of therapy, rather than testing for a cure at the regimen's end. This article does not cover the use of TB TMTs as surrogate endpoints in the clinical trial context.

Guidelines were initially informed by experiences during a systematic review of TB TMTs. Subsequently, a small content expert group was consulted for feedback on initial recommendations. After revision, feedback from substantive experts across sectors was sought.

The proposed considerations and recommendations for studies evaluating TB TMTs for use during the treatment in routine clinical care fall into eight domains. We provide specific recommendations regarding study design and recruitment, outcome definitions, reference standards, participant follow-up, clinical setting, study population, treatment regimen reporting, and index tests and data presentation. Overall, TB TMTs should be evaluated in a manner similar to diagnostic tests, but TB TMT accuracy must be assessed at multiple timepoints throughout the treatment course, and TB TMTs should be evaluated in study populations who have already received a diagnosis of TB. Study design and outcome definitions must be aligned with the developmental phase of the TB TMT under evaluation. There is no reference standard for TB treatment response, so different reference standards and comparator tests have been proposed, the selection of which will vary depending on the developmental phase of the TMT under assessment. The use of comparator tests can assist in generating evidence. Clarity is required when reporting of timepoints, TMT read-outs, and analysis results. Implementing these recommendations will lead to higher quality TB TMT studies that will allow data to be meaningfully compared, thereby facilitating the development of novel tools to guide individual TB therapy and improve treatment outcomes.